1 citations · 1 across the 9 of their papers we have counts for
5 papers · 1 filter
Variance-Reducing Couplings for Random Features
Isaac Reid, Stratis Markou, Krzysztof Choromanski +2
Random features (RFs) are a popular technique to scale up kernel methods in machine learning, replacing exact kernel evaluations with stochastic Monte Carlo estimates. They underpi…
General Graph Random Features
Isaac Reid, Krzysztof Choromanski, Eli Berger +1
We propose a novel random walk-based algorithm for unbiased estimation of arbitrary functions of a weighted adjacency matrix, coined universal graph random features (u-GRFs). This…
Repelling Random Walks
Isaac Reid, Eli Berger, Krzysztof Choromanski +1
We present a novel quasi-Monte Carlo mechanism to improve graph-based sampling, coined repelling random walks. By inducing correlations between the trajectories of an interacting e…
Quasi-Monte Carlo Graph Random Features
Isaac Reid, Krzysztof Choromanski, Adrian Weller
We present a novel mechanism to improve the accuracy of the recently-introduced class of graph random features (GRFs). Our method induces negative correlations between the lengths…
Simplex Random Features
Isaac Reid, Krzysztof Choromanski, Valerii Likhosherstov +1
We present Simplex Random Features (SimRFs), a new random feature (RF) mechanism for unbiased approximation of the softmax and Gaussian kernels by geometrical correlation of random…